论文标题
从智能手机摄像机步行的比赛中自动发现故障：奥林匹克奖牌和大学运动员的比较
Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
论文作者
论文摘要
自动故障检测是许多运动的主要挑战。在比赛中，根据规则，裁判在视觉上判断缺点。因此，在判断时确保客观性和公平性很重要。为了解决这个问题，一些研究试图使用传感器和机器学习来自动检测故障。但是，有一些与传感器附件和设备有关的问题，例如高速摄像头，与裁判的视觉判断以及故障检测模型的可解释性冲突。在这项研究中，我们提出了一种用于非接触式测量的故障检测系统。我们使用了根据多个合格裁判的判断进行训练的姿势估计和机器学习模型，以实现公平的错误判断。我们使用普通比赛的智能手机视频在包括东京奥运会奖牌获得者在内的智能手机视频中有故意的缺点。验证结果表明，所提出的系统的平均准确度超过90％。我们还透露，机器学习模型根据比赛的规则检测到故障。此外，奖牌获得者的故意故障步行运动与大学步行者不同。这一发现符合更通用的故障检测模型的实现。代码和数据可在https://github.com/szucchini/racewalk-aiudge中找到。
Automatic fault detection is a major challenge in many sports. In race walking, referees visually judge faults according to the rules. Hence, ensuring objectivity and fairness while judging is important. To address this issue, some studies have attempted to use sensors and machine learning to automatically detect faults. However, there are problems associated with sensor attachments and equipment such as a high-speed camera, which conflict with the visual judgement of referees, and the interpretability of the fault detection models. In this study, we proposed a fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified referees to realize fair fault judgement. We verified them using smartphone videos of normal race walking and walking with intentional faults in several athletes including the medalist of the Tokyo Olympics. The validation results show that the proposed system detected faults with an average accuracy of over 90%. We also revealed that the machine learning model detects faults according to the rules of race walking. In addition, the intentional faulty walking movement of the medalist was different from that of university walkers. This finding informs realization of a more general fault detection model. The code and data are available at https://github.com/SZucchini/racewalk-aijudge.
